Some recent FAS works [1]}, [2]}, [3]} consider each dataset as one domain and promote the domain generalization benchmark in FAS, which utilizes three datasets for training, and the remaining one as testing. As we aim to distinguish the patch type in the fine-grained manner, our proposed framework can be directly used to evaluate such benchmark without employing further generalization techniques (e.g., adversarial training or meta-learning). With access to more different patch types with more diverse capture devices, our framework is capable of learning more discriminative features through the patch recognition proxy task. There are four protocols in this benchmark: O&C&I to M, M&I&O to C, M&C&O to I, and M&C&I to O. During training, we directly combine the fine-grained patch classes from the three training datasets, which results in 18, 17, 22, and 21 classes, respectively.
